1 Comparing FPGA vs. Custom CMOS and the Impact on Processor Microarchitecture Henry Wong Vaughn Betz, Jonathan Rose

2 Processor Microarchitecture ● Microarchitecture: How to arrange circuits to make a processor ● Depends on how efficient the circuits are ● Which depends on the substrate – Custom CMOS – Standard Cell – FPGA

3 Goals ● Make good microarchitecture design choices for bigger and faster FPGA soft processors ● Much existing literature on processor design for custom CMOS implementation – Comparisons of overall area/delay between substrates exist – But relative building block costs vary up to two orders of magnitude on FPGA vs. Custom CMOS ● This work: compares building blocks and infer microarchitectural conclusions – Also applicable to circuits other than processors

4 What we're measuring 1. Focus on processors as the complete circuit – FPGA vs. Custom: Synthesize RTL for FGPA 2. Compare building block circuits that are often used in processors – SRAM, CAM, Multiplier, Adder, … 3. Infer how existing microarchitectures should be modified for FPGA

5 Methodology ● FPGA circuits synthesized through Quartus II 10.0 – Largest, fast speed grade, 65 nm Stratix III (3LS340) – Area calculated from FPGA tile areas – A few results are from literature ● Custom CMOS design examples found in literature – High-performance circuit design and layout are difficult and time consuming – Normalize to 65 nm: Ideal area scaling and ring oscillator delay scaling

6 Metrics ● Area – Still a key design constraint on FPGAs ● Delay ● Power or energy: Not considered here – Data not often published and testing conditions not standard. – FPGA users mostly spared responsibility for not melting the chip.

7 1. Processor Core Comparison ● Complete circuit serves as a reference point for sub-circuit measurements later

8 Processor Core Comparison ● SPARC T1 and T2, Intel Atom and Nehalem – Compare CMOS to FPGA implementations – Compare just one core, excludes large caches ● FPGA implementation used RTL optimized from the custom CMOS implementation – Atom and Nehalem results cited from literature

9 Processor Cores: Area ● Area ratio: FPGA/Custom area – 17-27x (Geomean 23x)

10 Processor Cores: Speed ● Speed ratio: Custom/FPGA fmax – 18-26x (Geomean 22x)

11 2. Building Block Comparisons ● Compare area and delay ● Will go through one example on SRAMs

12 Single-Port SRAM ● Custom: A few design examples from literature and data from the CACTI area and delay models ● FPGA: Four ways to build memory on Stratix III – M144K (2k x 72-bit) – M9K (256 x 36-bit) – MLAB (32 x 20-bit) – Registers and muxes ● Used (n x 32-bit) memories in this section

13 Single-Port SRAM Density ● Single-port density ratio: 2-5x (compare to 23x) – Partly due to FPGA's dual-ported memory blocks Hard SRAM blocks save area 2- 5x

14 Single-Port SRAM Fmax ● SRAMs 7-10x ratio for < 256 kbit (compare to 22x) ● Big arrays: stitching small blocks adds more delay 7- 10x

15 ● Density ratio: 7x for 2r1w, more write ports worse ● Fmax ratio is 9x-15x for 2r1w through 20r10w 7x: Replicate RAM twice for 2r1w143x: Registers and muxes for 4r2w 23x: Replicate RAM 8x for 4r2w Multiported SRAM Density (2kb)

16 Summary: Building Blocks ● Lower ratios are better for FPGA

17 Building Blocks ● Area dominates the differences between block types ● Multiplexers are slow ● SRAM bits are cheap – Multiported memories are expensive ● CAMs and muxes are expensive ● Hard adders/multipliers save area, but aren't fast ● Pipeline latches slightly faster ● These costs affect microarchitecture choices...

18 3. Processor Microarchitecture CAM Multiported RAM Multiplexers

19 SRAM Ports: Clustered RF ● Choose architecture to minimize register file ports – Clustered register files: One write port per cluster

20 Scheduler CAM: Intel P6 ● P6 to Nehalem ● Values stored in three places ● RS is a CAM that stores values

21 Scheduler CAM: AMD K7 ● AMD K7/K8/K10 ● Values stored in three places ● RS is a CAM that stores values

22 Physical Register File ● MIPS R10000, Intel P4, Sandy Bridge, AMD Bobcat ● Values stored in one place ● Scheduler CAM stores no operands PRF: Fewer multiported RAMs and smaller CAM

23 Reducing Bypass Muxes ● Two sets of bypass muxes per operation ● Multiple issue makes bypass muxes even bigger

24 Fusing Operations ● Chaining dependent operations: 3 muxes/2 ops – Fused multiply-add works especially well because incremental cost of second operation is small Point-to-point saves one bypass mux

25 Summary ● Need to measure cost of building block circuits to guide microarchitecture design choices – Relative area costs span 2 orders of magnitude ● Microarchitecture choices should reflect costs – Examples: Reduce RAM port count, CAM size, and multiplexers; Take advantage of cheaper ALUs – Use clustered physical register file, (no reservation stations); Explore fusing dependent operations together

26 Future Work ● Use these results to guide the design of a larger and higher-performance soft processor – Use existing microarchitecture literature as guidance, and adapt for FPGA substrate

27 Thank You!